Behavioral Problems Associated with Attention Deficit/Hyperactivity Disorder Among Students as Perceived by Elementary School Teachers in Riyadh

This study was aimed at measuring the differences of elementary school teachers’ perceptions of behavioral problems that appear in pupils with attention deficit hyperactivity disorder (ADHD). The study focused on measuring any statistically significance differences of some variables and their relationship to the extent of realization of teachers. These variables included gender, level of education, years of experience, teacher's current position, previous experience in teaching pupils with ADHD, and training. This research applied the descriptive approach, using a questionnaire. The study sample included 304 male elementary school teachers and 301 female elementary school teachers. The study found several results; no statistically significant differences at the 0.05 level or less regarding teachers’ perceptions of behavioral problems in pupils with ADHD and level of education. The study also showed statistically significant differences between the perceptions of teachers and gender, years of experience, teacher's current position, previous experience in teaching pupils with ADHD, and training

The Importance of Using Assistive Technology with Students with Intellectual Disabilities in Inclusive Education Schools

The study aimed to identify importance of using assistive technology with students with intellectual disabilities in inclusive education schools from the perspective of teachers and to reveal statistically significant differences in means of their responses about importance of using assistive technology in inclusive schools according to the following variables (number of students in classroom, degree of disability, years of experience, and attending training on using technology). The researchers used the descriptive method. The sample consisted of 134 female teachers in inclusive education schools in Riyadh and 106 teachers completed the questionnaire. Results showed that teachers agreed on the importance of using assistive technology with students with intellectual disabilities. Also, there were no statistically significant differences in participants’ perceptions about importance of using assistive technology in inclusive schools attributed to study variables

Reinforced concrete bridge damage detection using arithmetic optimization algorithm with deep feature fusion

Inspection of Reinforced Concrete (RC) bridges is critical in order to ensure its safety and conduct essential maintenance works. Earlier defect detection is vital to maintain the stability of the concrete bridges. The current bridge maintenance protocols rely mainly upon manual visual inspection, which is subjective, unreliable and labour-intensive one. On the contrary, computer vision technique, based on deep learning methods, is regarded as the latest technique for structural damage detection due to its end-to-end training without the need for feature engineering. The classification process assists the authorities and engineers in understanding the safety level of the bridge, thus making informed decisions regarding rehabilitation or replacement, and prioritising the repair and maintenance efforts. In this background, the current study develops an RC Bridge Damage Detection using an Arithmetic Optimization Algorithm with a Deep Feature Fusion (RCBDD-AOADFF) method. The purpose of the proposed RCBDD-AOADFF technique is to identify and classify different kinds of defects in RC bridges. In the presented RCBDD-AOADFF technique, the feature fusion process is performed using the Darknet-19 and Nasnet-Mobile models. For damage classification process, the attention-based Long Short-Term Memory (ALSTM) model is used. To enhance the classification results of the ALSTM model, the AOA is applied for the hyperparameter selection process. The performance of the RCBDD-AOADFF method was validated using the RC bridge damage dataset. The extensive analysis outcomes revealed the potentials of the RCBDD-AOADFF technique on RC bridge damage detection process

Survival-Based Treatment Planning Using Stage-Specific Machine Learning Models

The significance of prognostic survivability in determining optimal treatment strategies for critical illnesses is widely acknowledged. However, there has been a lack of emphasis on the advancement of treatment planning models based on survival outcomes within clinical decision support systems. The research presented in this paper proposes an innovative framework for the planning of treatment strategies based on survival outcomes in the context of multi-stage diseases, with the aim of effectively tackling this issue. Our proposed system aims to predict a comprehensive list of treatment combinations for cancer patients, specifically focusing on their expected survival outcomes. The proposed solution aims to enhance the decision-making process of medical professionals by providing them with comprehensive and comprehensible treatment recommendations. To conduct survivability classification and regression analysis for patients with identical cancer stages, a two-step approach is employed. This involves the development of stage-specific Machine Learning models using breast cancer data that includes treatment information. Based on a real dataset on cancer patients, we aim to investigate the performance of the models under different balancing strategies. Our contribution in this work is the formulation of a treatment planning inference system, which focuses on prognostic considerations. This system utilizes patient data and estimates the survivability associated with each treatment plan in order to predict the recommended course of action. This facilitates the integration of the developed survival prediction models into the process of treatment planning. Ultimately, the system generates visual representations that illustrate the comparative significance of different features, as well as the decision-making process employed by the model in order to yield easily comprehensible outcomes for a specific patient. The study presents experimental findings that illustrate the efficacy of our proposed framework in the domains of treatment planning and survival estimation

Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively

Improved Coyote Optimization Algorithm and Deep Learning Driven Activity Recognition in Healthcare

Healthcare is an area of concern where the application of human-centred design practices and principles can enormously affect well-being and patient care. The provision of high-quality healthcare services requires a deep understanding of patients’ needs, experiences, and preferences. Human activity recognition (HAR) is paramount in healthcare monitoring by using machine learning (ML), sensor data, and artificial intelligence (AI) to track and discern individuals’ behaviours and physical movements. This technology allows healthcare professionals to remotely monitor patients, thereby ensuring they adhere to prescribed rehabilitation or exercise routines, and identify falls or anomalies, improving overall care and safety of the patient. HAR for healthcare monitoring, driven by deep learning (DL) algorithms, leverages neural networks and large quantities of sensor information to autonomously and accurately detect and track patients’ behaviors and physical activities. DL-based HAR provides a cutting-edge solution for healthcare professionals to provide precise and more proactive interventions, reducing the burden on healthcare systems and improving patient well-being while increasing the overall quality of care. Therefore, the study presents an improved coyote optimization algorithm with a deep learning-assisted HAR (ICOADL-HAR) approach for healthcare monitoring. The purpose of the ICOADL-HAR technique is to analyze the sensor information of the patients to determine the different kinds of activities. In the primary stage, the ICOADL-HAR model allows a data normalization process using the Z-score approach. For activity recognition, the ICOADL-HAR technique employs an attention-based long short-term memory (ALSTM) model. Finally, the hyperparameter tuning of the ALSTM model can be performed by using ICOA. The stimulation validation of the ICOADL-HAR model takes place using benchmark HAR datasets. The wide-ranging comparison analysis highlighted the improved recognition rate of the ICOADL-HAR method compared to other existing HAR approaches in terms of various measures

Sex and national differences in internet addiction in Egypt and Saudi Arabia

Background: Understanding individual differences in psychology, and how they relate to specific addictions, may allow society to better identify those at most risk and even enact policies to ameliorate them. Internet addiction is a growing health concern, a research focus of which is to understand individual differences and the psychology of those most susceptible to developing it. Western countries are strongly overrepresented in this regard. Method: Here, sex and national differences in internet addiction are measured, using Young's ‘Internet Addiction Test,’ in two non-Western countries, Egypt and Saudi Arabia. >800 students aged 18 and 35 years (M = 20.65, SD = 1.48) completed a multidimensional internet addiction instrument. The instrument measures traits such as Withdrawal and Social Problems, Time Management and Performance and Reality Substitute. Results: Analyses revealed that males scored higher than females and Saudis higher than Egyptians on nearly all scales, including the total score. Factor analysis of the 20-item instrument revealed three factors, all exhibiting sex and culture differences. Conclusions: These findings add to the body of evidence that males are higher than females in problematic internet use, as they are in addictive behaviors in general. Our findings may also imply that restrictions on male-female interaction, which are more pronounced in Saudi Arabia, may elevate the prevalence of internet addiction. The internet is also easier and cheaper to access in Saudi Arabia than in Egypt

Sex and Culture Differences in Cultural Intelligence: A Study Comparing Saudi Arabians and Egyptians

Cultural Intelligence (CI) refers to the motivation and ability to understand and deal with cultural differences. As such, it is assumed to play a role in the effectiveness of social contact and communication between people from different cultures. Given its relevance to international relations, it is imperative to test which individual and group factors are associated with CI. Therefore, in the present study we examine cross-cultural and gender differences in CI. In one of their classes at their university, students (N = 829) from Egypt and Saudi Arabia completed a multidimensional measure of CI. The results showed an interesting pattern of interactions between country and gender, which indicated that Egyptian men did not significantly differ from co-national women, but Saudi men scored significantly lower than women. We suggest that the different patterns of results in the two countries may partly arise from different levels of exposure to different cultures and partly from subtle differences in the constitution of the samples. Knowledge of individual and group differences in cultural intelligence may potentially contribute to explaining differential levels of success in individuals or countries in dealing with cultural differences

Sex and Culture Differences in Cultural Intelligence: A Study Comparing Saudi Arabians and Egyptians

Cultural Intelligence (CI) refers to the motivation and ability to understand and deal with cultural differences. As such, it is assumed to play a role in the effectiveness of social contact and communication between people from different cultures. Given its relevance to international relations, it is imperative to test which individual and group factors are associated with CI. Therefore, in the present study we examine cross-cultural and gender differences in CI. In one of their classes at their university, students ( N  = 829) from Egypt and Saudi Arabia completed a multidimensional measure of CI. The results showed an interesting pattern of interactions between country and gender, which indicated that Egyptian men did not significantly differ from co-national women, but Saudi men scored significantly lower than women. We suggest that the different patterns of results in the two countries may partly arise from different levels of exposure to different cultures and partly from subtle differences in the constitution of the samples. Knowledge of individual and group differences in cultural intelligence may potentially contribute to explaining differential levels of success in individuals or countries in dealing with cultural differences